reduce

PySpark Dataframe cast two columns into new column of tuples based value of a third column

由 纵饮孤独 提交于 2019-12-04 18:49:13
As the subject describes, I have a PySpark Dataframe that I need to cast two columns into a new column that is a list of tuples based the value of a third column. This cast will reduce or flatten the dataframe by a key value, product id in this case, and the result os one row per key. There are hundreds of millions of rows in this dataframe, with 37M unique product ids. Therefore I need a way to do the transformation on the spark cluster without bringing back any data to the driver (Jupyter in this case). Here is an extract of my dataframe for just 1 product: +-----------+-------------------+-

How to implement a more general reduce function to allow early exit?

由 故事扮演 提交于 2019-12-04 13:59:43
问题 reduce (aka foldL in FP) is the most general iterative higher order function in Javascript. You can implement, for instance, map or filter in terms of reduce . I've used an imperative loop to better illustrate the algorithm: const foldL = f => acc => xs => { for (let i = 0; i < xs.length; i++) { acc = f(acc)(xs[i]); } return acc; }; const map = f => xs => { return foldL(acc => x => acc.concat([f(x)]))([])(xs); } let xs = [1, 2, 3, 4]; const inc = x => ++x; map(inc)(xs); // [2, 3, 4, 5] But

Java stream merge or reduce duplicate objects

由 非 Y 不嫁゛ 提交于 2019-12-04 11:14:30
问题 I need to generate a unique friend list from a list that can have duplicates by merging all duplicate entries into one object Example - Friends are fetched from different social feeds and put into 1 big list 1. Friend - [name: "Johnny Depp", dob: "1970-11-10", source: "FB", fbAttribute: ".."] 2. Friend - [name: "Christian Bale", dob: "1970-01-01", source: "LI", liAttribute: ".."] 3. Friend - [name: "Johnny Depp", dob: "1970-11-10", source: "Twitter", twitterAttribute: ".."] 4. Friend - [name:

Why don't I see any output from the Kafka Streams reduce method?

由 纵饮孤独 提交于 2019-12-04 08:42:16
Given the following code: KStream<String, Custom> stream = builder.stream(Serdes.String(), customSerde, "test_in"); stream .groupByKey(Serdes.String(), customSerde) .reduce(new CustomReducer(), "reduction_state") .print(Serdes.String(), customSerde); I have a println statement inside the apply method of the Reducer, which successfully prints out when I expect the reduction to take place. However, the final print statement shown above displays nothing. likewise if I use a to method rather than print , I see no messages in the destination topic. What do I need after the reduce statement to see

Map and Reduce Monad for Clojure… What about a Juxt Monad?

由 我的未来我决定 提交于 2019-12-04 08:11:13
问题 Whilst learning Clojure, I've spent ages trying to make sense of monads - what they are and how we can use them.... with not too much success. However, I found an excellent 'Monads for Dummies' Video Series - http://vimeo.com/20717301 - by Brian Marik for Clojure So far, my understanding of monads is that it is sort of like a macro in that it allows a set of statements to be written in a form that is easy to read - but monads are much more formalised. My observations are limited to two

Hadoop Spill failure

由 廉价感情. 提交于 2019-12-04 07:59:24
I'am currently working on a project using Hadoop 0.21.0, 985326 and a cluster of 6 worker nodes and a head node. Submitting a regular mapreduce job fails, but I have no idea why. Has anybody seen this exception before? org.apache.hadoop.mapred.Child: Exception running child : java.io.IOException: Spill failed at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.checkSpillException(MapTask.java:1379) at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.access$200(MapTask.java:711) at org.apache.hadoop.mapred.MapTask$MapOutputBuffer$Buffer.write(MapTask.java:1193) at java.io.DataOutputStream.write

JavaScript native groupBy reduce

由 只愿长相守 提交于 2019-12-04 05:41:37
问题 I am using JavaScript native reduce, however I want to slightly change in the grouping to get my desired result. I have an array as follows: const people = [ {name: "John", age: 23, city: "Seattle", state: "WA"}, {name: "Mark", age: 25, city: "Houston", state: "TX"}, {name: "Luke", age: 26, city: "Seattle", state: "WA"}, {name: "Paul", age: 28, city: "Portland", state: "OR"}, {name: "Matt", age: 21, city: "Oakland", state: "CA"}, {name: "Sam", age: 24, city: "Oakland", state: "CA"} ] I want

Reduce the HTTP Requests of 1000 images?

由 ε祈祈猫儿з 提交于 2019-12-04 04:20:24
I know this question might sound a little bit crazy, but I tough that maybe someone could come up with a smart idea: Imagine you have 1000 thumbnail images on a single HTML page. The image size is about 5-10 kb. Is there a way to load all images in a single request? Somehow zip all images into a single file… Or do you have any other suggestions in the subject? Other options I already know of: CSS sprites Lazy load Set Expire headers Downloads images across different hostnames There are only two other options I can think of given your situation: Use the "data:" protocol and echo a base64

reducelist in Python: like reduce but giving the list of intermediate results

由 生来就可爱ヽ(ⅴ＜●) 提交于 2019-12-04 03:00:16
You know the handy reduce function in Python. For example, you could use it to sum up a list like so (pretend there isn't the built-in sum ): reduce(lambda x,y: x+y, [1,2,3,4], 0) which returns (((0+1)+2)+3)+4 = 10. Now what if I wanted a list of the intermediate sums? In this case, [1,3,6,10] . Here's an ugly solution. Is there something more pythonic? def reducelist(f, l, x): out = [x] prev = x for i in l: prev = f(prev, i) out.append(prev) return out My favourite, if you're recent enough: Python 3.2.1 (default, Jul 12 2011, 22:22:01) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin

Java stream reduce

由 我与影子孤独终老i 提交于 2019-12-04 02:25:49
I have the following example data set that I want to transform / reduce using Java stream api based on direction's value Direction int[] IN 1, 2 OUT 3, 4 OUT 5, 6, 7 IN 8 IN 9 IN 10, 11 OUT 12, 13 IN 14 to Direction int[] IN 1, 2, OUT 3, 4, 5, 6, 7 IN 8, 9, 10, 11 OUT 12, 13 IN 14 code that I've written so far enum Direction { IN, OUT } class Tuple { Direction direction; int[] data; public Tuple merge(Tuple t) { return new Tuple(direction, concat(getData(), t.getData())); } } private static int[] concat(int[] first, int[] second) { int[] result = Arrays.copyOf(first, first.length + second
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